Added Face Recognition Script

Face_Recognition/Face_Data.ipynb

@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Required Imports\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Detecting face and saving data\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "detector = cv2.CascadeClassifier(\"./datasets/haarcascade_frontalface_default.xml\")\n",
+    "\n",
+    "name = input(\"Enter your name : \")\n",
+    "\n",
+    "frames = []\n",
+    "outputs = []\n",
+    "\n",
+    "while True:\n",
+    "\n",
+    "    ret, frame = cap.read()\n",
+    "\n",
+    "    if ret:\n",
+    "        faces = detector.detectMultiScale(frame,1.1,4)\n",
+    "\n",
+    "        for face in faces:\n",
+    "            x, y, w, h = face\n",
+    "\n",
+    "            cut = frame[y:y+h, x:x+w]\n",
+    "\n",
+    "            fix = cv2.resize(cut, (100, 100))\n",
+    "            gray = cv2.cvtColor(fix, cv2.COLOR_BGR2GRAY)\n",
+    "            cv2.imshow(\"My Face\", gray)\n",
+    "        cv2.imshow(\"My Screen\", frame)\n",
+    "        \n",
+    "\n",
+    "    key = cv2.waitKey(1)\n",
+    "\n",
+    "    if key == ord(\"q\"):\n",
+    "        break\n",
+    "    if key == ord(\"c\"):\n",
+    "        # cv2.imwrite(name + \".jpg\", frame)\n",
+    "        frames.append(gray.flatten())\n",
+    "        outputs.append([name])\n",
+    "\n",
+    "X = np.array(frames)\n",
+    "y = np.array(outputs)\n",
+    "\n",
+    "data = np.hstack([y, X])\n",
+    "\n",
+    "f_name = \"face_data.npy\"\n",
+    "\n",
+    "if os.path.exists(f_name):\n",
+    "    old = np.load(f_name)\n",
+    "    data = np.vstack([old, data])\n",
+    "\n",
+    "np.save(f_name, data)\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Face_Recognition/Face_Recog.ipynb

@@ -0,0 +1,87 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Required Imports\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Detecting face\n",
+    "data = np.load(\"face_data.npy\")\n",
+    "\n",
+    "# print(data.shape, data.dtype)\n",
+    "X = data[:, 1:].astype(int)\n",
+    "y = data[:, 0]\n",
+    "model = KNeighborsClassifier()\n",
+    "model.fit(X, y)\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "detector = cv2.CascadeClassifier(\"./datasets/haarcascade_frontalface_default.xml\")\n",
+    "while True:\n",
+    "\n",
+    "    ret, frame = cap.read()\n",
+    "\n",
+    "    if ret:\n",
+    "        faces = detector.detectMultiScale(frame,1.5,4)\n",
+    "\n",
+    "        for face in faces:\n",
+    "            x, y, w, h = face\n",
+    "\n",
+    "            cut = frame[y:y+h, x:x+w]\n",
+    "\n",
+    "            fix = cv2.resize(cut, (100, 100))\n",
+    "            gray = cv2.cvtColor(fix, cv2.COLOR_BGR2GRAY)\n",
+    "\n",
+    "            out = model.predict([gray.flatten()])\n",
+    "        \n",
+    "            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)\n",
+    "            cv2.putText(frame, str(out[0]), (x, y - 10), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 0, 0), 2)\n",
+    "            \n",
+    "            cv2.imshow(\"My Face\", gray)\n",
+    "\n",
+    "        cv2.imshow(\"My Screen\", frame)\n",
+    "\n",
+    "\n",
+    "    key = cv2.waitKey(1)\n",
+    "\n",
+    "    if key == ord(\"q\"):\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Face_Recognition/README.md

@@ -0,0 +1,22 @@
+# Face_Recognition
+This script helps you to recognize faces by a pre-trained ML model.
+# Quick Start
+- Clone this repository.
+
+      git clone https://github.com/geekquad/Image-Processing-OpenCV.git
+
+- Change Directory
+
+      cd Image-Processing-OpenCV
+      cd Face_Recognition
+
+- Run Face_data.ipynb file to collect data for model training
+
+ Input title of data.
+ Press `C` to Capture image and `Q` to exit window.
+
+ - Run Face_Recog.ipynb file for image recogntion
+
+
+# Screenshot 
+![](datasets/capture.PNG)